Magnetic Resonance Imaging Analysis Research Articles

Magnetic resonance imaging characterization of an α‐synuclein model of Parkinson's disease

AbstractParkinson's disease (PD) is primarily characterized by three histological hallmarks: dopaminergic neuronal degeneration, α‐synuclein accumulation and iron deposition. Over the last years, neuroimaging, particularly magnetic resonance imaging (MRI) has provided invaluable insights into the mechanisms underlying the disease. However, no imaging method has yet been able to translate α‐synuclein protein accumulation and spreading. Amongst the animal models mimicking the disease, the α‐synuclein rat, generated through the injection of human α‐synuclein, has been characterized in terms of behavioural and histological aspects but not thoroughly explored in MRI. The aim of this study is, therefore, to identify the radiological signature from several MRI sequences, while controlling for histological and behavioural characteristics. Rats were assessed for motor and cognitive functions over a 4‐month period. During this time, three MRI sessions, including both morphological and functional sequences, were conducted. Histological studies evaluated the three main hallmarks of PD. The progressive dopaminergic neurodegeneration and the spread of human α‐synuclein corresponded to the level of sensorimotor, attentional and learning deficits observed in this PD model. MRI analyses showed progressive structural abnormalities in the midbrain, diencephalon and several cortical structures, as well as a pattern of hyperconnectivity in the basal ganglia and cortical networks. The regions affected in imaging demonstrated the highest load of human α‐synuclein. This model's structural and functional MRI changes could serve as indirect indicators of α‐synuclein accumulation and its association with impaired non‐motor functions.

Anatomical Characteristics of Cervicomedullary Compression on MRI Scans in Children with Achondroplasia

This retrospective study assessed anatomical characteristics of cervicomedullary compression in children with achondroplasia. Twelve anatomical parameters were analyzed (foramen magnum diameter and area; myelon area; clivus length; tentorium and occipital angles; brainstem volume outside the posterior fossa; and posterior fossa, cerebellum, supratentorial ventricular system, intracranial cerebrospinal fluid, and fourth ventricle volumes) from sagittal and transversal T1- and T2-weighted magnetic resonance imaging (MRI) scans from 37 children with achondroplasia aged ≤ 4 years (median [range] 0.8 [0.1–3.6] years) and compared with scans from 37 children without achondroplasia (median age 1.5 [0–3.9] years). Mann–Whitney U testing was used for between-group comparisons. Foramen magnum diameter and area were significantly smaller in children with achondroplasia compared with the reference group (mean 10.0 vs. 16.1 mm [p < 0.001] and 109.0 vs. 160.8 mm2 [p = 0.005], respectively). The tentorial angle was also steeper in children with achondroplasia (mean 47.6 vs. 38.1 degrees; p < 0.001), while the clivus was significantly shorter (mean 23.5 vs. 30.3 mm; p < 0.001). Significant differences were also observed in myelon area, occipital angle, fourth ventricle, intracranial cerebrospinal fluid and supratentorial ventricular volumes, and the volume of brainstem protruding beyond the posterior fossa (all p < 0.05). MRI analysis of brain structures may provide a standardized value to indicate decompression surgery in children with achondroplasia.

Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.

Several studies have confirmed the potential value of applying radiomics to predict prognosis of breast cancer. However, the tumor segmentation in these studies depended on delineation or annotation of breast cancer by radiologist, which is often laborious, tedious, and vulnerable to inter- and intra-observer variability. Automatic segmentation is expected to overcome this difficulty. Herein, we aim to investigate the value of automatic segmentation-based multi-modal radiomics signature and magnetic resonance imaging (MRI) features in predicting disease-free survival (DFS) of patients diagnosed with invasive breast cancer. This retrospective multicenter study included a total of 643 female patients with invasive breast cancer who underwent preoperative ultrasound (US) and MRI for prognostic analysis. Data (n = 480) from center 1 were divided into training and internal testing sets, while data (n = 163) from centers 2 and 3 were analyzed as the external testing set. We developed automatic segmentation frameworks for tumor segmentation by deep learning. Then, Least absolute shrinkage and selection operator Cox regression was used to select features to construct radiomics signature, and corresponding radiomics score (Rad-score) was calculated. Finally, six models for predicting DFS were constructed by using Cox regression and assessed in terms of discrimination, calibration, and clinical usefulness. The multi-modal radiomics signature combining intra- and peri-tumoral radiomics signatures of US and MRI achieved a higher C-index in the internal (0.734) and external (0.708) testing sets than most other radiomics signatures in predicting DFS, and successfully stratified patients into low- and high-risk groups. The multi-modal clinical imaging model combining the multi-modal Rad-score and clinical traditional MRI model-score resulted in a higher C-index (0.795) than other models in the external testing set, and it had a better calibration and higher clinical benefit. This study demonstrates that the multi-modal radiomics signature derived from automatic segmentations of US and MRI is a promising risk stratification biomarker for breast cancer, and highlights that the appropriate combination of multi-modal radiomics signature, clinical characteristics, and MRI feature can improve performance of individualized DFS prediction, which might assist in guiding decision-making related to breast cancer.

Motion and magnetic field inhomogeneity correction techniques for chemical exchange saturation transfer (CEST) MRI: A contemporary review.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B0 and B1 +) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B0 and B1 + inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B0 and B1 + inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

Abstract 4145884: Left ventricular 3D-sphericity is associated with 92 genetic loci

Introduction: Left ventricular (LV) sphericity is a measure of the degree to which the LV blood pool resembles a sphere. Analyses of cardiovascular magnetic resonance imaging (MRI) in the MESA cohort have shown that LV sphericity is an important mode of variation in cardiac structure that is associated with ischemia, heart failure, and atrial fibrillation. A recent genome-wide association study (GWAS) of a two-dimensional estimate of sphericity in 38,000 UK Biobank participants identified 4 genetic loci (near PDZRN3, HLA, PLN, and ANGPT1). Genetic contributions to three-dimensional (3D) sphericity remain unknown. Aims: To measure 3D sphericity from cardiovascular MRI at scale and characterize the genetic contributions to 3D sphericity. Methods: Deep learning models were constructed to perform semantic segmentation in the short- and long-axis view from cardiovascular MRI in UK Biobank. These models were applied to all participants with MRI. Poisson surface reconstruction was used to build 3D meshes of the left ventricle. Sphericity was estimated at end-systole and end-diastole as the area of a perfect sphere with the same volume as the left ventricle, divided by the estimated surface area from the mesh. Heritability was estimated with ldsc; GWAS was conducted with REGENIE. Results: Sphericity was estimated in 68,978 individuals, of whom 3,096 were excluded due to atrial fibrillation or atrial dysfunction, and 2,686 were excluded during genetic quality control; 63,196 participants were included in the GWAS. Ldsc heritability was 29.7% for sphericity in diastole and 26.0% for sphericity in systole. 49 genetic loci were associated with 3D sphericity in diastole as were 64 in systole (92 distinct loci). 46 of these loci were unique to sphericity and not found for other LV traits, including loci near the genes encoding calsequestrin 2, troponin T2, palladin, supervillin, and angiopoietins 1 and 2. Shared loci included those near RXFP4, encoding a relaxin-family peptide receptor, and MICU2 (previously implicated in cardiac relaxation). Conclusion: Left ventricular sphericity is heritable, with genetic underpinnings distinct from standard LV measurements.

Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.

To evaluate the ability of magnetic resonance imaging (MRI)-based clustering analysis to predict the pathological response to neoadjuvant chemotherapy (NACT) in patients with primary high-grade osteosarcoma. Twenty-two patients were included in this retrospective study. All patients underwent MRIs before and after NACT. The entire tumor volume was manually delineated on post-contrast T1-weighted images and subsegmented into three clusters using the K-means algorithm. Histogram-based parameters were calculated for each lesion. The response to NACT was obtained from the histopathological assessment of the tumor necrosis rate following resection. The Mann-Whitney test was used to compare poor and fair-to-good responders. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the optimal parameters. At baseline, poor responders showed a significantly larger volume of cluster1 (Vol1) than fair-to-good responders (p = 0.038). After NACT, they exhibited a lower 10th percentile (P10) and kurtosis (p = 0.038 and 0.002, respectively). Vol1 at baseline and P10 after NACT had an AUC of 77% (95% CI 56-98%). The kurtosis after NACT had the best discriminative power, with an AUC of 89.7% (95% CI 75-100%). The MRI-based histogram and clustering analysis provided a good ability to differentiate between poor and fair-to-good responders before and after NACT. Further investigations using larger datasets are required to corroborate our findings.

EPCO-03. METABOLIC INSIGHT INTO GLIOMA HETEROGENEITY: MAPPING WHOLE EXOME SEQUENCING TOIN VIVO IMAGING WITH STEREOTACTIC LOCALIZATION AND DEEP LEARNING

Abstract Intra-tumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by genomic alterations. While multiparametric imaging captures spatial and functional variations enabling ITH characterization, it falls short in assessing the metabolic activities underpinning phenotypic differences. This gap stems from the challenge of integrating easily accessible, co-located pathology and genomic data with metabolic insights. This study presents a multi-faceted approach combining stereotactic biopsy with clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. The aim is to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma who underwent multiparametric MR scans (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During craniotomy, at least one stereotactic biopsy was collected from each patient, with the screenshots of sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard ROC analyses were used to evaluate detection, with AUC calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75±0.11, with individual AUCs as high as 0.96 for IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. An average AUC of 0.85 across the five mutations was achieved using the combination of T1W, T2W-FLAIR, and ADC. These results suggest the possibility of predicting exome-wide mutation events from non-invasive, in vivo imaging by combining stereotactic localization of glioma samples with a semi-parametric deep learning method. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

Supra-Prophylactic Doses of Enoxaparin Reduces Fibrin Deposition Without Exacerbation of Intracerebral Hemorrhage in a Rat Model of Penetrating Traumatic Brain Injury.

Deep vein thrombosis and pulmonary embolism prophylaxis is an important part of trauma care. Despite an increased risk of thrombotic complications, the use of venous thrombosis chemoprophylaxis in penetrating traumatic brain injury (pTBI) patients is met with reluctance from neurosurgeons because of concern for the exacerbation of intracerebral hemorrhage. The objective of this study was to provide initial pre-clinical evidence of the effects of Lovenox (LVX) administration following pTBI with significant intracerebral hemorrhage. Sprague-Dawley rats received a penetrating ballistic-like brain injury. Animals were randomly divided into two groups following injury: LVX (25 mg/kg) or vehicle (VEH, saline). LVX or vehicle was administered subcutaneously beginning 24 h after the injury and continued daily for 7 days post-injury. A neurological assessment was performed daily and magnetic resonance imaging (MRI) was performed at baseline, 1, 2, 3, and 7 days post-injury. Following the final MRI, brains were isolated and prepared for histological analysis. Thromboelastography demonstrated dramatic anticoagulation effects which were confirmed by significant increases in partial thromboplastin time (p < 0.001). Daily neurological assessment revealed no worsening of functional deficits following LVX treatment. MRI analysis demonstrated no differences in cerebral edema or intracranial hemorrhage volumes between treatment groups at any tested post-injury time points. However, LVX elicited a significant reduction in fibrin deposition in the ipsilateral striatum and lesion site at 7 days post-injury (p < 0.05). Serum levels of beta-amyloid were decreased at 7 days following LVX treatment (p < 0.05) which may indicate neuroprotective effects but was not correlated to brain levels. The results presented indicate that administration of LVX at a dose capable of inducing anticoagulation is safe in a rodent model of pTBI without exacerbation of intracerebral hemorrhage within the first 7 days of injury.

Analysis of ultrasound and magnetic resonance imaging characteristics of kaposiform hemangioen dothelioma

ObjectiveThe present study aims to investigate the ultrasound and magnetic resonance imaging (MRI) characteristics of kaposiform hemangioen dothelioma (KHE).MethodsA retrospective analysis was conducted on the clinical data of children diagnosed with KHE through postoperative pathology. Patients were divided into two groups: the KHE group and the KHE with Kasabach-Merritt Phenomenon (KMP) group (KMP group). Laboratory indicators, ultrasound, and MRI data were collected and analyzed statistically to summarize the imaging characteristics of the disease.ResultsThe levels of platelets and fibrinogen in the KHE group were significantly higher than those in the KMP group, while D-dimer levels, prothrombin time, and activated partial thromboplastin time were lower (P < 0.05). Ultrasound characteristics comparison revealed that lesions extending to the fat layer (42.47% vs. 54.24%) and invading the muscle layer (38.36% vs. 69.49%) were less common in the KHE group compared to the KMP group, with the lesion diameter being smaller in the KHE group (P < 0.05). The Adler grading predominantly showed Grade II (45.21%) in the KHE group, whereas Grade III (93.22%) was more prevalent in the KMP group (P < 0.05). MRI analysis indicated that the incidence of lesions invading the muscle layer and the presence of flow voids were lower in the KHE group compared to the KMP group (P < 0.05).ConclusionKHE patients with KMP exhibit lesions that are more prone to extending into the fat layer and invading the muscle layer, with larger diameters and abundant blood flow. Additionally, the MRI images of the lesions may exhibit flow voids.

Textual Analysis of CT and MR images in bladder cancer: The promise and pitfalls in systematic review

Introduction: Textual analysis is a new stage in the development and improvement of radiology. Textual analysis (TA) or radiomics is a method of in-depth study of medical images using specialized computer programs. It`s based on the quantification of invisible features and provides tools for researchers to gain deep insights into tumor morphology.A number of mathematically extracted texture features (TF) can be correlate with morphological, molecular and genotypic characteristics of the studied field of interest. Aim: Analysis of the literature and comparative evaluation of publications to perform challenges and possibilities of texture analysis in bladder cancer. Materials and methods: We searched for scientific publications in the PubMed and Google Scholar information and analytical systems for 2016-2024 by keywords: «bladder cancer», «bladder cancer radiomics», «mri bladder cancer»». As a result, 40 articles were selected for analysis. Results: Our review demonstrates the methodology, challenges, opportunities and prospects of TA CT and MP imagesin bladder cancer.Numerous studies have demonstrated high accuracy of radiomics in local staging and estimation of tumor morphology, as well as in prediction of clinical outcomes. Conclusion: TA in controversial clinical cases is a useful way of increasing theinformationobtainable frommedical images. Further validation of these methodis required however.

The "Hedgehog-Halo Sign" Is Associated with Gait Symptom Severity and Tap Response in Normal Pressure Hydrocephalus.

Reduced cerebrospinal fluid (CSF) clearance may play a vital role in the pathogenesis of normal pressure hydrocephalus (NPH), but the radiologic marker is yet to be elucidated. This open-label study presents two novel neuroimaging biomarkers based on enlarged perivascular spaces (ePVS) of the sub-insular territory: the Hedgehog and Hedgehog-Halo (H-H) sign, designed to predict gait symptom severity and tap response in NPH. We retrospectively reviewed 203 patients with possible NPH with baseline magnetic resonance imaging and gait analyses before and after lumbar puncture (LP). The Hedgehog/H-H sign was scored using T2-weighted images. The clinical severity at baseline and post-tap gait improvement was compared in patients with and without Hedgehog/H-H sign. The association between Hedgehog/H-H sign and post-tap gait outcomes was assessed using multivariate regression. The diagnostic performance of Hedgehog/H-H sign was compared with conventional radiological markers. Patients with H-H showed higher global disability and more severe gait impairment than those without any signs. Following LP, patients with Hedgehog/H-H sign significantly improved in various gait parameters, unlike those with neither sign. Additionally, sub-insular ePVS was significantly associated with post-tap gait improvement after adjusting covariates. Finally, the Hedgehog/H-H sign showed a higher performance than conventional markers in predicting post-tap gait response. The Hedgehog/H-H sign is a useful neuroimaging biomarker related to the severity and tap response in NPH. This biomarker can be readily applied in clinical practice before undergoing LP, independent of conventional radiological signs. Further research is warranted to determine applicability in predicting post-shunt gait response.

Myocardial deformation in multisystem inflammatory syndrome in children: layer-specific cardiac MRI insights from a pediatric cohort.

Multilayer strain magnetic resonance imaging (MRI) analysis offers detailed insights into myocardial mechanics and cardiac function by assessing different layers of the heart muscle, enabling a comprehensive understanding of cardiac involvement. This study aims to explore cardiac strain differences between patients with multisystem inflammatory syndrome and a control group at medium-term follow-up, utilizing a layer-specific cardiac magnetic resonance imaging (CMR) approach. In this retrospective study, patients with multisystem inflammatory syndrome in children (MIS-C) and a group of controls who had undergone cardiac magnetic resonance (CMR) imaging were selected and included. CMR was performed 30days after discharge (range 34-341days) for MIS-C patients. TrufiStrain research prototype software (Siemens Healthineers AG, Erlangen, Germany) was used for automated myocardial segmentation and strain calculation, to measure radial strain (RS), circumferential strain (CS), and longitudinal strain (LS) at the epicardial, mid-wall, and endocardial levels. Statistical analysis included Shapiro-Wilk tests, Student t-tests, and Mann-Whitney U tests, ANOVA, and regression analysis, maintaining a significance level of α = 0.05. The study cohort consisted of 32 MIS-C patients (≤ 18years; 14 females) and 64 control participants (≤ 18years; 24 females). Median interval to CMR post diagnosis was 142days (range 34-341) with normal CMR findings for all patients. The mean age of the two groups was similar (MIS-C: 14.2years; controls: 14.1years, P = 0.49). There were no significant differences in height (MIS-C: 164.7cm; controls: 163.9cm, P = 0.84), weight (MIS-C: 68.2kg; controls: 59.4kg, P = 0.11), or body surface area (MIS-C: 1.7 m2; controls: 1.7 m2, P = 0.41). Global strain measurements showed no significant differences between the groups (global LS MIS-C patients - 16.2% vs - 15.7% in controls (P = 0.23); global RS 27.8% in MIS-C patients vs 29.5% in controls (P = 0.35); and global CS - 16.7% in MIS-C patients vs - 16.8% in controls (P = 0.92)). Similarly, layer-specific strain analysis across the endocardial (LS values of - 17.7% vs - 16.8% (P = 0.19), RS of 23.1% vs 24.8% (P = 0.25), and CS of - 19.9% vs - 19.9% (P = 0.92)), epicardial (LS - 14.9% vs - 14.5% (P = 0.31), RS of 31.2% vs 33.1% (P = 0.29), and CS of - 14.1% vs - 14.2% (P = 0.75)), and midmyocardial (LS - 16.5% vs - 16.3% (P = 0.18), RS 29.3% vs 31.8% (P = 0.31), and CS - 17.0% vs - 17.2% (P = 0.95)) levels revealed no significant disparities. The only notable finding was the reduced apical radial strain in MIS-C patients compared to controls (global RS MIS-C 12.4% vs 17.4% in controls, P = 0.03; endocardium RS MIS-C 4.9% vs 10.31% in controls, P = 0.01; epicardial RS MIS-C 17.7% vs 22.6% in controls, P = 0.02; and midmyocardium RS MIS-C 12.5% vs 17.9% in controls, P = 0.02). This study demonstrates that MIS-C does not significantly impact global or layer-specific myocardial strain values, as assessed by CMR, compared to a control group. The lower apical radial strain in MIS-C patients indicates a potential localized myocardial involvement.

Syntactic and semantic specialization in 9- to 10-year-old children during auditory sentence processing

Prior literature has debated whether syntax is separable from semantics in the brain. Using functional magnetic resonance imaging and multi-voxel pattern analysis, our previous studies investigated brain activity during morpho-syntactic versus semantic processing. These studies only detected semantic specialization in activation patterns and no syntactic specialization in 5- to 6-year-old and 7- to 8-year-old children. To examine if older children who have mastered morpho-syntactic skills would show specialization for syntax, the current study examined 64 9- to 10-year-old children using the same design and analyses. We observed that only the left IFG pars opercularis was sensitive to syntactic but not semantic information, supporting the hypothesis that this region serves as a core region for syntax. In addition, the left STG which has been implicated in the integration of semantics and syntax, as well as the left MTG and IFG pars triangularis which have been implicated in semantics, were sensitive to both semantic and syntactic information with no evidence of specialization. These findings suggest a lexicalized view of syntax, which argues that semantically sensitive regions are also critical regions for syntactic processing during language comprehension.

Analysis of sagittal plane cine magnetic resonance imaging for measurement of pancreatic tumor residual motion during breath hold and evaluation of gating margins used in radiotherapy treatment.

In pancreatic radiotherapy, residual tumor motion during treatment increases the risk of toxicity. Cine imaging acquired during magnetic resonance guided radiotherapy (MRgRT) enables real-time treatment gating in response to anatomical motion, which can reduce this risk; however, treatment gating can negatively impact the efficiency of treatment. This study aimed to quantify the extent of residual tumor motion during breath hold and evaluate the appropriateness of the treatment gating margins used in current clinical practice. Cine imaging acquired during pancreatic MRgRT of 11 patients on the ViewRay MRIdian was analyzed. The total duration of treatment analyzed was 12 h 13 min. Improved methods for processing and analyzing cine imaging were developed: breath holds were systematically separated with frequency analysis, residual motion was measured with consideration of both the tracking structure contour and centroid, and residual motion measurements were supported by phantom measurements of image scaling, resolution, and noise. Residual motion was measured at angles 0°, 45°, 90°, and 135° to the superior-inferior (SI) direction. Total residual motion was measured by combining directional measurements. The minimum tracking structure displacement resolvable through cine imaging was found to be 1.5mm; therefore, residual motion analysis was limited to 1.5mm spatial resolution. Total residual motion was contained within margins ±1.5, ±3, and ±4.5mm with mean percentage frequencies of 97.0%, 91.1%, and 67.8%. Most residual motion was observed in the SI direction, and significantly more residual motion was measured for the tracking structure contour than the centroid. The results demonstrate that patients are largely able to maintain breath hold positions to within a 3mm margin, thus provide evidence that supports the use of a 3mm gating margin in clinical practice. Residual motion frequently exceeded 1.5mm so a reduction in gating margin would have an undesirable impact on treatment efficiency.

A novel adjunctive diagnostic method for bone cancer: Osteosarcoma cell segmentation based on Twin Swin Transformer with multi-scale feature fusion

BackgroundOsteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma. MethodsIn this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion. ResultsThe experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings. ConclusionThe development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.

Persistence of Labral Tears and Resolution of Paralabral Cysts with 2-year MRI analysis after Periacetabular Osteotomy (PAO) for Hip Dysplasia

To evaluate the labrum status and paralabral cysts on preoperative and postoperative magnetic resonance imaging (MRI) in patients with developmental dysplasia of the hip (DDH) who underwent periacetabular osteotomy (PAO). A retrospective review was conducted using prospectively collected data from patients who had undergone primary PAO between June 1, 2021 and May 30, 2022. Inclusion criteria were an age range of 16 to 55 years and a minimum follow-up of two years. MRI was utilized to evaluate the preoperative and postoperative conditions of the labrum and the presence of paralabral cysts. Furthermore, the correlations between the presence of paralabral cysts and demographic and radiographic variables were investigated. Additionally, patient-reported outcomes (PROs), minimal clinically important difference (MCID), and complications were reported during the final follow-up evaluation. A total of 23 patients were included in the final analysis, with an average follow-up of 26.6 ± 3.4 months (range, 24-32 months). All patients diagnosed with symptomatic DDH exhibited labrum tears. MRI scans revealed paralabral cysts in ten out of twenty-four (41.6%) patients, with a significant correlation noted between the presence of cysts and preoperative LCEA (ρ=-0.649; P=0.001), ACEA (ρ=-0.637; P=0.001), Tönnis angle (ρ=0.593; P=0.002) and age (ρ=-0.444; P=0.034). Postoperative MRI scans at one year showed resolution of all paralabral cysts, while labral tears were found to persist. At the end of the follow-up period, most patients improved considerably in PROs compared to preoperative values. The probabilities of achieving the MCID were 91.3% on the HOS-ADL, 78.2% on the HOS-SSS, 91.3% on the mHHS, 73.9% on the iHOT-12, and 69.6% on the VAS pain scale. All symptomatic patients with DDH had labral tears and a high prevalence of paralabral cysts, which were corrected with reduced acetabular coverage. After PAO surgery, patients experienced notable enhancements in PROs, with resolution of paralabral cysts but persistence of labral tears.

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI

BackgroundFemoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipeline for ONFH based on magnetic resonance imaging (MRI) radiomics.MethodsIn the segmentation model development dataset, T1-weighted MRI of 222 hips from two hospitals were retrospectively collected and randomly split into training (n = 190) and test (n = 32) sets. In the prognosis prediction model development dataset, 206 hips were also retrospectively collected from two hospitals and divided into training set (n = 155) and external test set (n = 51) according to data source. A deep learning model for automatic lesion segmentation was trained with nnU-Net, from which three-dimensional regions of interest were segmented and a total of 107 radiomics features were extracted. After intra-class correlation coefficients screening, feature correlation coefficient screening and Least Absolute Shrinkage and Selection Operator regression feature selection, a machine learning model for ONFH prognosis prediction was trained with Logistic Regression (LR) and Light Gradient Boosting Machine (LightGBM) algorithm.ResultsThe segmentation model achieved an average dice similarity coefficient of 0.848 and an average 95% Hausdorff distance of 3.794 in the test set, compared to the manual segmentation results. After feature selection, nine radiomics features were included in the prognosis prediction model. External test showed that the LightGBM model exhibited acceptable predictive performance. The area under the curve (AUC) of the prediction model was 0.851 (95% CI: 0.7268–0.9752), with an accuracy of 0.765, sensitivity of 0.833, and specificity of 0.727. Decision curve analysis showed that the LightGBM model exhibited favorable clinical utility.ConclusionThis study presents an automated pipeline for predicting femoral head collapse in ONFH with acceptable performance. Further research is necessary to determine the clinical applicability of this radiomics-based approach and to assess its potential to assist in treatment decision-making for ONFH.

Quantitative Characterization of Surfactant Displacement Efficiency by NMR—Take the Tight Oil of Chang 8 Member of Yanchang Formation in Fuxian Area, Ordos Basin, as an Example

Surfactant flooding is a pivotal technique for enhancing oil recovery efficiency. The Chang 8 member of the Yanchang Formation in the Ordos Basin exemplifies a quintessential tight oil reservoir. Specifically, 47.9% of wells yield less than 0.1 t/d, 27.0% produce between 0.1 and 0.2 t/d, and 18.8% generate outputs ranging from 0.2 to 0.3 t/d, while only a mere 6.3% exceed production rates of over 0.3 t/d, indicating minimal efficacy of water flooding development in this context. In this study, we conducted an extensive investigation into the geological characteristics of the Yanchang 8 reservoir within the Ordos Basin, leading to the identification and evaluation of three surfactants based on their interfacial tension properties. The optimal injection concentration was determined through on-line displacement nuclear magnetic resonance imaging analysis that refined surface activity conducive to developing the Chang 8 member, ultimately resulting in increased spread volume and enhanced crude oil production from individual wells. The results indicate the following: (1) The interfacial tension of NP-10, FSD-952, and GPHQ-1 at concentrations of 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% exhibited a pattern of initial decrease followed by an increase. The mass concentration corresponding to the minimum interfacial tension for NP-10 is identified as 0.1%, which is also the case for GPHQ-1; however, for FSD-952, this occurs at a concentration of 0.4%. Among the surface-active agents NP-10, GPHQ-1, and FSD-952, GPHQ-1 demonstrated the lowest interfacial tension value at an impressive measurement of 0.0762 mN/m. (2) When the displacement of the 0.1% GPHQ-1 surfactant reaches 10 PV, the displacement efficiency improves from 69.69% to 76.36%, representing an increase of 6.67%. The minimum pore size observed during GPHQ-1 surfactant displacement is 0.01 μm. In contrast, when the displacement of the NP-10 surfactant at a concentration of 0.1% reaches 10 PV, the efficiency rises from 68.32% to 72.02%, indicating an enhancement of 3.7%. The corresponding minimum pore size for NP-10 surfactant displacement is recorded at 0.02 μm. Furthermore, when the displacement of the FSD-952 surfactant at a concentration of 0.4% achieves 10 PV, its efficiency increases from 69.93% to 74.77%, reflecting an improvement of 4.81%. The minimum pore size associated with the activated portion of FSD-952 is noted as being approximately 0.03 μm.

Contribution of automated hippocampal volume measurements to radiological diagnosis in cases of mesial temporal sclerosis

Aims: Epilepsy, particularly drug-resistant epilepsy in adults, is often caused by Mesial Temporal Sclerosis (MTS), which can develop after brain injury from febrile illnesses or trauma. Magnetic Resonance Imaging (MRI) is crucial for diagnosing MTS, although challenges such as patient movement and mild or bilateral hippocampal atrophy can complicate the diagnosis. T1-weighted hippocampal volume measurements are effective in detecting MTS, with recent software advancements enabling automatic hippocampal segmentation. This study compared the hippocampal volumes and indices between adults with MTS and a control group of similar age and sex in the Turkish population. This study aimed to highlight structural differences in the hippocampus associated with MTS. Methods: This study involved a retrospective review of cranial MRIs scans from patients with MTS, confirmed through histopathological examination after epilepsy surgery. To ensure unbiased comparisons, a control group was selected using propensity score matching by age and gender. Two experienced neuroradiologists independently assessed the MRIs findings for MTS without knowledge of hippocampal volumetric data. Hippocampal volumes were measured using FreeSurfer software and standardized using the hippocampal volume index (HVI) and hippocampal asymmetry index (HAI). Results: In our study of 38 patients, MTS was found in 55.2% of patients on the right side and in 44.8% of patients on the left side, with no bilateral cases. Visual MRI analysis identified MTS in 84.2% of patients, with an area under of curve (AUC) of 0.921. Automatic volumetry detected MTS in 23 patients with an AUC of 0.791. Combining both methods, MTS was diagnosed in 33 patients, with an AUC of 0.922. Conclusion: Automated volumetric analyses have been shown to enhance the detection of hippocampal volume loss in patients with MTS.

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.